The Biggest Shift in Local Discovery in 20+ Years

Local commerce is undergoing its most significant transformation in over two decades. For years, discovery was dominated by Google: consumers searched, scanned, ranked results, compared options, and made decisions. That behaviour still exists, but it no longer defines how discovery works at scale.

The consumer doesn’t search anymore. Increasingly, their personal AI does.
Our research shows a seismic shift in both frequency and flow. Consumers are searching more often and across more surfaces, with over half now turning directly to AI-driven systems such as ChatGPT, Gemini, and Perplexity. These platforms do not return ten blue links. They interpret intent, aggregate signals from across the web, and surface recommendations on the user’s behalf, before brands are actively evaluated.

This marks a fundamental shift in power and pace. Discovery is faster, more automated, and increasingly mediated by AI agents rather than humans manually comparing options. The risk for brands is clear: if AI systems cannot confidently understand, trust, or recommend a location or brand, visibility simply disappears. The opportunity is equally large.

This playbook exists to help multi-location brands and enterprise marketing leaders respond to that shift. Drawing on insights from 2,000+ consumers across the US, UK, France, and Germany, alongside 200 senior-level US marketers and third-party research, it explains how discovery is evolving and what AI systems now rely on to make decisions and recommend your brand to consumers.

More importantly, it provides practical guidance. It outlines the new architecture of discovery and the new operating models for the AI era. For CMOs and marketing teams, the question is not whether this shift is coming, but how quickly they can adapt to the new landscape where AI-mediated discovery is the default.

The End of the Human-Led Search Era

For decades, local discovery followed a predictable pattern. Consumers searched online, compared multiple results, and made decisions themselves. Search engines returned links, and humans did the work of interpretation.

That pattern is changing. In 2026, local discovery will increasingly take place within personal AI agents, rather than conventional search engines. These context-aware AI agents, capable of knowing individual preferences and expectations, will deliver deeply personalised answers to consumer queries. The consumer's task of manually sifting through search results is being replaced. Personal AI agents now mediate discovery by interpreting intent, synthesising information from multiple sources, and presenting ranked options, summaries, or direct and personalised answers. Even when consumers say they are “using Google,” their experience is often shaped by AI-powered overviews, map results, and summarised recommendations rather than traditional link-by-link evaluation. Conductor’s benchmarking shows that AI Overview (AIO) results appear in roughly one-quarter of all searches analysed, illustrating how frequently AI is part of the discovery surface marketers must optimise for.

Consumer survey data further highlights this shift. 51% of consumers indicated that their usage of AI tools has increased over the past 6 months, and 56.1% of consumers say they read AI-generated overviews when they appear. Only 24.2% say they actively avoid AI summaries, indicating that AI-driven answers are already part of mainstream discovery behavior.

The Collapsed Consumer Journey

As AI takes on more interpretation and comparison work, the traditional funnel has shortened dramatically.

Where discovery once unfolded across several stages, now it is compressed into a single, high-intent moment where intent, interpretation, and action happen in quick succession.

Brands face a clear implication: visibility gaps directly cause lost revenue at the peak of consumer intent, where AI dictates which businesses are visible during early decision-making.

Consumers most often turn to AI for comparison and narrowing choices. 53.1% use AI to compare product features, while 42.9% use it to find restaurants with specific qualities, such as dietary needs, atmosphere, or proximity. AI is also used for planning, with 32.2% relying on it to build itineraries or structured plans, especially when multiple variables are involved. Even for simple lookups, AI is increasingly used, with 25.8% using AI to find basic information like phone numbers or addresses.

As AI becomes embedded at each of these moments, it increasingly shapes not just how consumers search, but which options are even considered. By the time a consumer validates a choice, AI has already filtered, summarised, and prioritised a small set of businesses based on the signals it can interpret. This shift elevates AI from to a powerful gatekeeper of discovery. The question is not just to what extent AI influences the journey, but how accurately it represents the brands competing within it.

What This Means for Brands

AI is becoming the primary gatekeeper of discovery, and with it comes both a material opportunity and a growing threat for location-based brands. The very brand identity that companies have spent years and billions building is now being interpreted, reshaped, and in some cases distorted by AI systems that decide what is surfaced, summarised, and recommended.

The risk is already visible. A significant majority of brands now experience negative sentiment skew in AI-generated responses, inconsistent business information across AI models, or factual errors and misstatements. These are not edge cases. They are signals that AI systems are struggling to reconcile fragmented, outdated, or incomplete inputs at scale. When AI cannot establish confidence, it fills the gaps with approximation.

This is because AI does not surface results the way traditional search engines do. Brands do not “rank” in AI-generated answers in a relatively stable or predictable order. Instead, AI systems reconstruct an understanding of a business based on the signals they can access, interpret, and align. That reconstruction is only as strong as the underlying data.

Local signals play a decisive role. Listings accuracy, reviews, photos, attributes, and location-specific content all influence how AI summarises and represents a brand. When those inputs are consistent and high quality, AI produces clear, confident recommendations. When they are fragmented, the output reflects that fragmentation, often in ways brands do not control or even see.

For multi-location brands, this creates a critical exposure point. With hundreds or thousands of locations, small data issues are multiplied at scale. An incorrect opening hour, a missing attribute, or an outdated photo does not remain isolated. It becomes part of the AI’s collective understanding of the brand. Over time, this erodes what can be described as “local truth at scale.” AI systems, designed to prioritise the most reliable and consistent information, will favor competitors whose local signals are cleaner and more unified, regardless of brand size or awareness.

Consumer behavior reinforces this dynamic. AI summaries are frequently read, but they are rarely treated as definitive. Only a small minority of consumers trusts an AI-generated summary without validation. Most move immediately to review platforms, maps, or source links to confirm what they see. This means AI does not replace trust signals. It amplifies them. Any weakness in local reputation or information quality is exposed faster and more visibly.

The implication for brands is clear. AI visibility is not a new channel to optimise in isolation. It is the outcome of how well local data, reputation, and content are governed and maintained across the entire digital ecosystem AI draws from. As AI becomes more deeply embedded in discovery, the cost of poor local signals rises sharply, and the margin for error narrows.

For brands that act, the opportunity is significant. AI rewards clarity, consistency, and credibility at scale. Brands that establish a reliable source of local truth, bolster their reputations, engage with consumers digitally, and maintain these activities continuously, are the ones that will control their representation in AI-driven discovery. For those who do not, AI will still tell their story. It just may not be the one they intended and that could be extremely costly.

The Strategy to Win in AI Search

With AI now acting as the primary interpreter and gatekeeper of discovery, brands must move beyond traditional optimisation and adopt a new strategic layer designed specifically for AI-driven understanding: Generative Engine Optimisation (GEO).

GEO addresses how AI systems interpret and summarise businesses. These systems rely on structured, consistent, and entity-rich data to form an understanding of what a brand offers, where it operates, and why it should be recommended. Listings, attributes, reviews, photos, and location signals all contribute to this interpretation.

Consumer behavior reinforces the importance of this layer. AI summaries are frequently read, but rarely treated as definitive. Most consumers continue by checking review platforms, maps, or source links, which place greater weight on the underlying data AI draws from. When that data is incomplete or inconsistent, the resulting summaries reflect those gaps.

SEO Is the Foundation, Not the Strategy

GEO does not replace Search Engine Optimisation (SEO). It extends it, ensuring that the information feeding AI-driven discovery is accurate, consistent, and aligned across locations. SEO remains essential as it provides the baseline visibility that allows brands to be discovered across digital surfaces.

What has changed is its role. Traditional search results increasingly sit beneath AI-generated answers, summaries, and recommendations that interpret information and make recommendations before consumers engage further. These experiences are already visible through AI Overviews and emerging AI-led search modes.

This shift is widely recognised within the local search community. As Darren Shaw, Founder of Whitespark, explains:

Crucially, this evolution does not invalidate strong SEO fundamentals:

“If you were doing good SEO before, then you should not need to change much.”

SEO continues to underpin discovery, but it does not govern the entire experience on its own as arguably it once did. As AI-driven discovery expands beyond any single channel or tactic, brands need a way to operationalise visibility, trust, and performance across every location.

1. Source of Truth — Location Data

AI systems reward certainty. The first pillar is establishing a verified, structured, and authoritative source of truth for every location.

This means ensuring that core business facts such as name, address, hours, services, attributes, and categories are accurate, consistent, and continuously maintained across the entire digital ecosystem. Listings, maps, directories, and owned properties must align because AI models synthesise information from multiple sources rather than trusting a single one.

When location data is fragmented or outdated, AI-generated summaries inherit those flaws. When it is consistent and structured, AI systems can confidently represent what a business does, where it operates, and whether it meets a user’s intent. Technical SEO underpins this pillar, ensuring data is crawlable, structured, and accessible, but the objective extends beyond search engines to any system interpreting local entities.

To establish location-specific sources of truth, meticulously manage your business profiles (like Google Business Profile and Apple Business Connect) and your website’s local pages. These profiles are the primary digital front door for high-intent local customers and are critical for AI search visibility, acting as the verified source of location representation and recommendations. 

Websites provide high-quality, unique data for AI models to learn from, cite, and enable multimodal search. Notably, 41.6% of consumers visit a company’s website before deciding to visit a location, highlighting its importance to human consumers as well as AI systems.

Ensuring accurate, complete, and actionable profiles and webpages is the most critical step in any AI search strategy, providing the structured data AI systems need for accurate recommendations.

Key actions:

• Standardise core location data.
  Ensure hours, addresses, services, and attributes are accurate and consistent across all platforms. AI systems aggregate signals from multiple sources; inconsistencies reduce confidence and suppress visibility.
• Validate geospatial accuracy.
  Correct map pins and coordinates are critical. AI weights proximity and navigation signals heavily when recommending local options for high-intent queries.
• Supply rich visual signals.
  Maintain current, high-quality photos across profiles and pages. Visual assets help AI validate legitimacy, category relevance, and real-world presence.
• Expose clear action paths.
  Enable structured actions such as call, book, and directions. These signals reinforce intent alignment and are frequently referenced in AI-generated recommendations.
• Maintain presence on authoritative directories.
  Trusted third-party platforms strengthen entity confidence and provide corroborating signals AI systems rely on for verification.
• Structure location content for AI interpretation.
  Reflect “near me” and conversational intent naturally in location pages, FAQs, and service descriptions to align with how AI parses user queries.
• Implement LocalBusiness schema at scale.
  Structured data allows AI and search systems to accurately interpret hours, services, pricing, and availability without ambiguity.
• Create dedicated, indexable location pages.
  Location-specific pages help AI connect brand entities to local context, services, and neighborhoods, strengthening relevance in AI-mediated discovery.

2. Context and Relevance Engineering — Dynamic Content Strategy

AI evaluates relevance in context. The second pillar focuses on providing living proof of relevance through content that reflects real customer needs, intent, and scenarios.

This includes FAQs, service descriptions, location-specific pages, reviews, and hyperlocal content that explains what a business offers, and why it is the right choice for a given situation or set of requirements. Content must be refreshed, expanded, and structured so AI systems can extract meaning, connect attributes, and understand nuance.

As consumer behavior becomes more conversational and intent-driven, AI relies on this contextual layer to filter and prioritise options. Content that is generic, thin, or disconnected from location realities limits how effectively a brand can be surfaced in AI-mediated discovery.

Key actions:

• Engineer intent-aligned FAQs and service content.
  Create structured, location-specific answers to common questions so AI systems can match nuanced intent with clear, authoritative responses.
• Continuously generate and refresh reviews.
  Maintain a steady flow of recent feedback to provide AI with up-to-date sentiment, experience signals, and proof of ongoing relevance.
• Respond to reviews at scale.
  Consistent, timely responses signal operational reliability and customer care, reinforcing trust signals AI uses to evaluate relevance and credibility.
• Localise offers and incentives in structured formats.
  Publish location-specific promotions and attributes that AI can associate with value, availability, and revisit intent.
• Publish hyperlocal content tied to real-world context.
  Reflect neighborhoods, services, and use cases to help AI understand where and why a location is relevant, not just what it offers.
• Align social content with location entities.
  Connect social profiles and posts to specific locations so engagement and proof points reinforce AI’s understanding of local relevance.
• Earn third-party validation. “Best of” lists, guides, and local features provide authoritative signals for AI systems to reference.
• Refresh content continuously, not periodically.
  Update FAQs, attributes, and descriptions as services, seasons, and demand change to keep AI interpretations current and accurate.

3. Orchestration Capability — Outcome at Scale

The third pillar is orchestration: the ability to execute, adapt, and distribute signals across locations in real time.

AI search rewards brands that operate as coordinated systems rather than collections of isolated locations. Orchestration capability brings together data, content, reviews, and engagement signals, supported by AI agents that assist with execution at scale. These agents can monitor changes, generate updates, respond to feedback, and help maintain consistency across hundreds or thousands of locations.

Without orchestration, even strong data and content decay quickly. With it, brands can maintain continuous readiness, respond to shifts in demand or AI interpretation, and ensure that improvements in one area are reflected everywhere they matter.

Key Actions:

• Centralise control, enable local execution.
  Define global standards for data, content, and responses while allowing local teams to adapt within clear guardrails that AI systems can interpret consistently.
• Deploy AI agents as operators, not experiments.
  Use AI to monitor changes, generate updates, respond to reviews, and maintain accuracy continuously across locations.
• Continuously monitor AI visibility and representation.
  Track how locations appear in AI-generated answers, summaries, and recommendations to identify drift, gaps, or emerging opportunities.
• Close the loop between insight and execution.
  Ensure that changes in data, reviews, or content are propagated across all relevant platforms and discovery surfaces without delay.
• Coordinate signals across systems and teams.
  Align listings, websites, reviews, social content, and engagement tools so AI receives consistent, reinforcing signals from every source.
• Adapt in real time to demand and interpretation shifts.
  Respond quickly to seasonality, local events, operational changes, or AI model updates that affect relevance and visibility.
• Measure outcomes, not just activity.
  Evaluate orchestration effectiveness based on visibility, trust signals, engagement, and real-world performance across locations.

Together, these components define the strategy to win in AI search and a repeatable operating model for multi-location brands. A trusted source of location truth provides certainty. Contextual content establishes relevance. Orchestration turns both into sustained, measurable outcomes. SEO remains the foundation beneath them, but success in the AI era depends on how effectively these pillars work together to shape how brands are understood, trusted, and recommended at scale. This demands a shift in how marketing teams at MLBs operate.

What Marketing Teams Must Do Differently in 2026

The new questions leaders must answer: governance, readiness, and investment.

AI-mediated discovery is reshaping not only how brands are found, but fundamentally how marketing organisations must operate. For two decades, search was the undisputed center of gravity for all digital marketing, influencing everything from budget allocation (SEM, SEO) and team structures (SEO specialists) to customer funnels, key performance indicators (rankings, clicks, conversion rates), customer journey mapping, and technology infrastructure.

Discovery is now shaped by systems that continuously interpret, refresh, and reweight information based on real-time intent. As a result, marketing leaders face a new set of questions that require a profound evolution of the marketing role and organisation. Who owns local truth? How ready is the organisation to operate continuously? And where should investment shift to reflect how discovery actually works?

The era of AI-mediated discovery demands a complete restructuring of teams and responsibilities. The function must now involve a broader array of people—digital marketers, content creators, social media specialists, and dedicated AI search specialists—leading to the creation of new roles (like the GEO Specialist) and even entirely new teams. This shift necessitates new skill sets centered on workflow management, cross-functional orchestration, and agentic AI. Budgets are already shifting dramatically, with 40% of respondents reporting they invest more than 50% of their marketing budget in geo-centric strategies. New metrics (Share of Voice, Inclusion Rate, citations) are replacing old ones, creating a need for new, integrated tools—like Uberall and the GEO Studio—to manage local complexity at scale.

As AI aggregates signals from listings, websites, reviews, and third-party platforms, clear governance becomes a prerequisite for visibility. When ownership is unclear, inconsistency follows, and AI systems struggle to form a confident view of the brand.

Effective teams operate with a clear governance model. Central teams define standards for data, content, and measurement, while local teams execute within guardrails that preserve relevance. The core challenge often revolves around collaboration. AI search performance relies on signals from multiple teams, including marketing, customer services, content creation, social media, and SEO specialists. Without a unified structure, conflicting signals proliferate across locations and platforms. This is critical because AI models reward consistency and precision, making the need for an all-in-one platform crucial to streamline cross-functional collaboration.

This challenge is already visible. In our B2B survey, 61.5% of marketing leaders describe managing local digital presence as complex or very complex, and nearly 80% say four or more teams influence local visibility. In an AI-mediated ecosystem, unclear governance doesn't just slow execution or directly affects how often and how consistently a brand is surfaced.

Visibility within AI-generated answers is inherently dynamic. Models continuously update sources, reinterpret signals, and rebalance recommendations. Brands that treat AI search as a one-time optimisation quickly lose ground. Those that adopt continuous monitoring, iteration, and optimisation steadily expand their presence.

Readiness, therefore, is not just strategic, it is operational. Yet for many organisations, the biggest barriers are structural. In our survey, 25% of marketing leaders cite fragmented systems and data sources as the largest capability gap preventing success in AI-led search.

This fragmentation shows up most clearly in day-to-day execution. The top pain point (37%) is keeping business information accurate across an expanding set of platforms, followed by ensuring accuracy across AI-driven experiences (49%) and updating business information across multiple channels at scale (39.5%). As AI systems increasingly pull from distributed and real-time data sources, even small inconsistencies can immediately impact visibility at the moment of intent.

While experimentation with AI tools is widespread, execution at scale remains the gap. All respondents say they would see strong value in an AI orchestration layer, one that can monitor performance, diagnose issues, and automatically execute local optimisations. This signals a shift in expectations: teams are no longer just looking for AI-powered insights, but for AI-powered operations.

AI also needs to move beyond experimentation. Increasingly, AI agents act as operators, managing updates, generating responses, and supporting optimisation at scale. When integrated into core workflows, these agents reduce manual effort and improve responsiveness. When treated as isolated tools, their impact remains limited. As Ana Martinez, Chief Technology Officer, Uberall, explains:

“Agentic AI goes beyond automation. These systems can reason, plan, and act independently toward defined goals and outcomes.”

Looking ahead, marketers are prioritising local content capabilities as a core pillar of readiness. The most important capabilities for 2026 include scaling localised content dynamically, automating content creation, and benchmarking content performance against competitors, reinforcing the need for systems that can operationalise content, data, and optimisation continuously.

The final question is whether investment matches the scale of change. Despite growing awareness of AI’s role in discovery, 83.5% of marketing leaders report that their overall budgets have not changed. The shift, therefore, is not about spending more. It’s about spending differently, reallocating resources from campaign-based media toward content, data, and optimisation.

Where budgets are evolving, the pattern is clear. One marketing leader explained:

“We have reallocated some of our traditional advertising budget to AI search-related channels to test their actual impact on improving high-intent traffic and conversion efficiency.”

This shift is also reshaping how organisations structure spend. Some teams have created new budget lines dedicated to data acquisition, preparation, and management, recognising that AI visibility depends as much on data infrastructure as on marketing execution.

Measurement: the challenge is no longer tracking. It’s proving impact

Unlike earlier digital shifts, most teams are already tracking AI visibility. In our survey, over 95% of marketing leaders say they monitor whether their brand is mentioned or cited in AI-generated answers, and 68.5% treat share of voice in AI chatbots as a primary KPI.

The challenge has moved beyond measurement itself to attribution and ROI. More than 30% of respondents cite a lack of clear attribution as a major pain point, making it difficult to determine how much traffic, revenue, or conversion can be credited to AI-driven discovery. As a result, many teams struggle to prove the business impact of AI search to leadership, slowing investment decisions and organisational buy-in.

In 2026, the most effective marketing teams will be those that align investment with how discovery actually works. They will establish clear governance over local truth, build operational readiness for continuous visibility, and measure success using AI-native indicators such as Share of Voice and brand mention frequency. Those that do will turn AI-mediated discovery into a durable advantage. Those who do not will find their visibility shaped by systems they are not equipped to manage.

Conclusion:

Orchestrating Local Commerce in the AI Era

Local commerce has entered a decisive moment. The changes outlined in this playbook are not incremental, and they are not theoretical. They reflect a fundamental shift in how discovery works, how decisions are made, and how value is created at the local level.

Consumers are searching more frequently, acting faster, and relying on AI-generated answers, assistants, and summaries. The journey from intent to action has compressed into moments, mediated by systems that interpret, filter, and recommend before a human ever compares options directly. Trust signals such as reviews, accurate information, and real-world proof still matter, but the path to those signals is increasingly controlled by AI-driven intermediaries.

At the same time, marketing leaders recognise that AI search and generative discovery are reshaping the competitive landscape. Industry benchmarks show rising investment, growing confidence in GEO initiatives, and increasing reliance on integrated platforms. Yet readiness remains uneven. Many organisations struggle with fragmented data, siloed teams, limited measurement, and operating models designed for a slower, campaign-led world. This gap between consumer behavior and organisational capability is widening.

For multi-location brands, the implications are amplified. Every inconsistency scales with outdated listings, missing attributes, or weak trust signals being replicated across hundreds or thousands of locations and interpreted by machines at speed. In an AI-mediated environment, these gaps are profound. They are surfaced, summarised, and reinforced across discovery surfaces that increasingly shape which brands are considered and how they are perceived.

This is why AI search cannot be treated as another channel to optimise or another experiment to run. It demands a different way of operating. SEO remains foundational, but it is now only part of what is imperative for local visibility. Generative Engine Optimisation shapes how AI systems

The direction of travel is clear. AI will increasingly mediate local search. The brands that succeed will be the ones that build local truth and contextual relevance at scale, maintain it relentlessly, and orchestrate it across the systems that now shape discovery.